A current stem cell hypothesis for breast cancer holds that mutations occur in the normal stem cell lineage from which the various differentiated cell types of the breast derive. These cancer stem cells divide asymmetrically, leading either to expansion of the stem cell population, or to differentiated daughter cells. According to this hypothesis, breast cancer arises when a combination of mutations and epigenetic modifications that subverts normal control mechanisms accumulate in one of the stem cell lineages. Intratumoral heterogeneity is implied by this mechanism because the mutations occur sporadically with respect to the growth of the stem cell population. We propose to track the evolution of intratumoral genetic heterogeneity in single tumors, and use this data to model tumor development by means of oncogenetic trees. Most of the branches on this oncogenetic tree will correspond to aberrant developmental pathways that do not progress to aggressive cancer. At least one branch, however, terminates in aggressive disease, that is, the cancer stem cell lineage. The path connecting this terminal node with the root of the tree (i.e. normal stem cells) traces the progression pathway. Further analysis of genomic sequences in branches that intersect with this pathway will reveal the temporal sequence of mutations corresponding to tumor progression. We will develop efficient ways of generating oncogenetic trees, and test the temporal sequence of a few mutations in several tumors. This will be done by using extensive microdissection and measurement of loss of heterozygosity (LOH) in widely sampled regions of the tumor. High- throughput microdissection has been solved, but there is no sufficiently high-throughput method for analysis of LOH. This problem will be solved by converting the genomes of microdissected pieces into low complexity representations (LCRs), spotting these LCRs on glass slides, and probing with probes directed toward Single Nucleotide Polymorphisms, to assess allelic imbalance in thousands of microdissected samples, simultaneously. Every microdissected piece of the tumor can then be related to every other piece by using the LOH data to build an oncogenetic tree. This tree reports on the history of the tumor, in that early LOH branch near the base of the tree, while later LOH branch closer to the "leaves" of the tree. Using this tree, we will be able to determine the temporal order in which mutations, differential expression, and epigenetic changes occur during tumor development. We will examine the timing of several mutations that are frequently observed in breast cancer. In the longer term, focusing on small regions of a tumor avoids missing small but important mutations that high-throughput discovery platforms such as microarrays cannot detect above background, due to intratumoral heterogeneity. This approach may lead to diagnostic and prognostic markers or therapeutic targets for different stages in progression. We propose several high-throughput mutation detection schemes that will allow us to build oncogenetic trees with the necessary efficiency to make the oncogenetic tree approach practical. PUBLIC HEALTH RELEVANCE: Only a small part of a breast cancer tumor is capable of causing metastatic disease, and these "cancer stem cells" are difficult to identify and characterize. By tracing the history of mutations as they develop and disseminate through the tumor, we will be able to identify diagnostic and prognostic markers, as well as potential therapeutic targets for progressively more aggressive forms of the disease.